AMyGDA

Description

This is a python3 module that takes a photograph of a 96 well plate and assesses each well for the presence of bacterial growth (here Mycobacterial tuberculosis). Since each well contains a different concentration of a different antibiotic, the minimum inhibitory concentration, as used in clinical microbiology, can be determined.

The development of this software was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) to aid the CRyPTIC project, but the software is agnostic to the plate design so could be easily adopted to other types or designs of 96-well (or even 384-well) plates.

Example image of a 96-well plate that has been analysed by AMyGDA. A yellow circle means the software has classified that well as containing bacterial growth.

AMyGDA was developed and tested using version 3.4.0 of OpenCV. If you do not have sudo access on your machine you can install this (and any other python module) in your $HOME directory using the following command

This provides a neat way of storing and discovering metadata for each image using the native filesystem. It is not essential for the operation of AMyGDA, but the code would need re-factoring to remove this dependency. Again it can be installed using pip

$ pip install datreant

Note that datreant works best if each image is containing within its own folder. datreant automatically stores all metadata associated with each image within a JSON file in the same location as the input file.

Tutorial

The code is structured as a python module; all files for which can be found in the amygda/ subfolder.

ls
LICENCE.md amygda/ setup.py
README.md examples/ bin/
config/

You may see other folders like build/ if you are run the setup.py script. To run the tutorial move into the examples/ sub-folder

$ cd examples/
$ ls
sample-images/

analyse-plate-with-amygda.py is a simple python file showing how the module can be used to analyse a single image. The fifteen images shown in Figure S1 in the Supplement of the accompanying paper (see above) are provided so you can reconstruct Figures S2, S3, S4 & S12. The images are organised as follows

$ ls sample-images/
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

$ ls sample-images/01/
image-01-raw.png

To process and analyse a single image using the default settings is simply

The hidden .datreant folder contains two JSON files. categories.json contains all the MICs and other metadata about the plate and both can be automatically discovered and read using the datreant module to make systematic analyses simpler.

image-01-mics.txtcontains the same information as the JSON file but in a simpler format that is easier for humans to read.

image-01-arrays.npz contains a series of numpy arrays that specify e.g. the percentage growth in each well

image-01-raw.png is the original image of the plate.

image-01-msf.jpg is a JPEG of the plate following mean shift filtering

image-01-clahe.jpg is a JPEG of the plate following mean shift filtering and then a Contrast Limited Adaptive Histogram Equalization filter to improve contrast and equalise the illumination across the plate.

image-01-filtered.jpg is a JPEG of the plate following both the above filtering operations and a histogram stretch to ensure uniform brightness.

image-01-growth.jpg adds some annotation; specifically the locations of the wells are drawn, each well is labelled with the name and concentration of drug and wells which AMyGDA has classified as containing bacterial growth are highlighted with a coloured circle.

To see the other options available for the analyse-plate-with-amygda.py python script

$ ./python analyse-plate-with-amygda.py --help
usage: analyse-plate-with-amygda.py [-h] [--image IMAGE]
[--growth_pixel_threshold GROWTH_PIXEL_THRESHOLD]
[--growth_percentage GROWTH_PERCENTAGE]
[--measured_region MEASURED_REGION]
[--sensitivity SENSITIVITY]
[--file_ending FILE_ENDING]
optional arguments:
-h, --help show this help message and exit
--image IMAGE the path to the image
--growth_pixel_threshold GROWTH_PIXEL_THRESHOLD
the pixel threshold, below which a pixel is
considered to be growth (0-255, default=130)
--growth_percentage GROWTH_PERCENTAGE
if the central measured region in a well has
more than this percentage of pixels labelled as
growing, then the well is classified as growth
(default=2).
--measured_region MEASURED_REGION
the radius of the central measured circle, as a
decimal proportion of the whole well
(default=0.5).
--sensitivity SENSITIVITY
if the average growth in the control wells is
more than (sensitivity x growth_percentage),
then consider growth down to this sensitivity
(default=4)
--file_ending FILE_ENDING
the ending of the input file that is stripped.
Default is '-raw'

Adapting for different plate designs

AMyGDA is written to be agnostic to the particular design of plate, or even the number of wells on each plate. The concentration (or dilution) of drug in each well is defined by a series of plaintext files in

config/

For example the drugs on the UKMYC5 plate (a variant of the Thermo Fisher MYCOTB plate) is defined within the

Adding a new plate design is simply a matter of creating new files specifying the drug, concentration and dilution of each well. Note that changing the number of wells at present also involves specifying the well_dimensions when creating a PlateMeasurement object. Currently this defaults to (8,12) i.e. a 96-well plate in landscape orientation. As an example, the configuration files for the UKMYC6 plate, which is the successor to the UKMYC5 plate, are included although all the provided examples are of UKMYC5 plates.